Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes

Author:

Kan Yingxin1,Jiang Limin12,Guo Yan3,Tang Jijun24,Guo Fei5

Affiliation:

1. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China

2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

3. Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S

4. School of Computational Science and Engineering, University of South Carolina, Columbia, U.S

5. School of Computer Science and Engineering, Central South University, Changsha, China

Abstract

Abstract Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

Reference66 articles.

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